Ocular Artifacts realization through optimized scheme

Brain Computer Interface (BCI) recommended for online real-time processing of EEG signals. Hence, the recording system’s accuracy improved by nullifying of developed artifacts. The goal of the proposed work is to develop an optimized model for detecting and minimizing ocular artifacts. In the propos...

Full description

Saved in:
Bibliographic Details
Published in:Journal of ambient intelligence and humanized computing 2023-10, Vol.14 (10), p.13269-13283
Main Authors: Sahoo, Santosh Kumar, Mohapatra, Sumant Kumar
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial Intelligence
Brain research
Computational Intelligence
Datasets
Decomposition
Deep learning
Discrete Wavelet Transform
Efficiency
Electroencephalography
Empirical analysis
Engineering
Eye movements
Formability
Human-computer interface
Independent component analysis
Methods
Original Research
Principal components analysis
Robotics and Automation
User Interfaces and Human Computer Interaction
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Brain Computer Interface (BCI) recommended for online real-time processing of EEG signals. Hence, the recording system’s accuracy improved by nullifying of developed artifacts. The goal of the proposed work is to develop an optimized model for detecting and minimizing ocular artifacts. In the proposed work, Discrete Wavelet Transform (DWT) and Pisarenko harmonic decomposition are used for decomposing the signals. Then the features are extracted by Principal Component Analysis (PCA) and Independent Component Analysis (ICA) techniques. After feature collection, an Optimized Deformable Convolutional Network (ODCN) is used for the recognition of ocular artifacts from EEG input signals. When artifacts are sensed, the moderation method is executed by applying Empirical Mean Curve Decomposition (EMCD) followed by ODCN for noise optimization in EEG signals. Conclusively, the spotless signal is reconstructed by an application of inverse EMCD. The proposed method has achieved a higher performance than that of conventional methods.
ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-022-03783-3